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Long-term changes in the seasonality of Baltic sea level Cover

Long-term changes in the seasonality of Baltic sea level

Tellus
Volume 68 (2016): Issue 1
By:  and    
Open Access
|Dec 2016
Figures & tables

Figures & Tables

Fig. 1

Map with the locations of the tide gauge stations in the Baltic Sea (°) and grid points of 20th century reanalysis (⋄) and ERA-Interim reanalysis (⋆).

Table 1. Analysed monthly tide gauge records (1900–2012)

Station nameLon. (°E)Lat. (°N)Missing values (%)
Wismar (WIS)11.4653.900.15Warnemünde (WAR)12.1054.170.15Gedser (GED)11.9354.570.98Hornbæk (HOR)12.4656.102.18Kungsholmsfort (KUN)15.5956.100.08Ölands Norra Udde (OLA)17.1057.370Stockholm (STO)18.0859.320Helsinki (HEL)24.9760.150.15Ratan (RAT)20.9063.990.23
Fig. 2

Mean seasonal cycle (1900–2012) and associated standard deviation computed for each tide gauge station (solid line) and estimated by linear regression from 20th century reanalysis atmospheric parameters (dashed line).

Fig. 3

As in Fig. 2 for the period 1979–2012 using atmospheric parameters from the ERA-Interim reanalysis.

Table 2. Linear regression models for the mean seasonal cycle of sea level and reanalysis atmospheric parameters

20th century 1900–201220th century 1979–2012ERA-interim 1979–2012
StationModelAdj. R2 (%)R2 ModelAdj. R2 (%)ModelAdj. R2 (%)
WISu-wind38air T28air T30WARu-wind53u-wind35u-wind30GEDu-wind59u-wind43u-wind50HORu-wind82u-wind68u-wind46KUNu-wind89u-wind845u-wind80OLAu-wind85u-wind87u-wind87STOPressure81Pressure87u-wind84HELPressure79Pressure79Pressure61RATPressure80u-wind83u-wind84
Fig. 4

Seasonal sea level cycle at Stockholm derived by (a) continuous wavelet transform (CWT), (b) discrete wavelet transform (DWT), (c) auto-regressive-based decomposition (AR), (d) singular spectrum analysis (SSA) and (e) empirical mode decomposition (EMD).

Fig. 5

Seasonal sea level cycle estimated by the DWT method.

Fig. 6

Seasonal cycle amplitude estimated from the DWT method.

Table 3. Linear regression models for the interannual changes in the annual amplitude of sea level and reanalysis atmospheric parameters

20th century 1900–201220th century 1979–2012ERA-interim 1979–2012
StationModelAdj. R2 (%)ModelAdj. R2 (%)ModelAdj. R2 (%)
WISNAO2air T12air T14WARu-wind9NAO13NAO13GEDu-wind17NAO17NAO17HORu-wind58u-wind51u-wind42KUNu-wind65u-wind62u-wind51OLAu-wind46u-wind44u-wind35STOPressure43Pressure35u-wind46HELPressure53Pressure52u-wind31RATPressure48Pressure47Pressure30
Fig. 7

Yearly seasonal cycle maximum estimated from the DWT method.

Fig. 8

Yearly seasonal cycle minimum estimated from the DWT method.

Fig. 9

MODWT-based multi-resolution decomposition of the Stockholm record (top) into additive components from high-frequency to long term scales.

Table 4. MODWT-based variance decomposition (%)

StationHigh-frequency (2–4 months)Semi-annual (4–8 months)Annual (8–16 months)Interannual (16–32 months)
WIS36253010WAR31263211GED37262810HOR26223511KUN26283511OLA23283612STO23293612HEL23283713RAT21283813
DOI: https://doi.org/10.3402/tellusa.v68.30540 | Journal eISSN: 3035-9554
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Language: English
Page range: 30540 - 30540
Submitted on: Nov 27, 2015
Accepted on: May 11, 2016
Published on: Dec 1, 2016
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
sea level,
Baltic Sea,
seasonality,
wavelet transform

© 2016 Susana M. Barbosa, Reik V. Donner, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 68 (2016): Issue 1
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